Which machine learning frameworks does this skill support?

The skill is designed to work with common Python data science frameworks, specifically supporting scikit-learn for classification and regression tasks.

How does the skill decide between classification and regression?

The skill performs an initial data analysis phase where it identifies the target variable and its distribution to determine the most appropriate model type for the dataset.

Can I use this for time-series forecasting?

While this specific skill focuses on general ML, it is part of a larger suite of Nixtla plugins designed to generate TimeGPT pipelines and time-series services.

Does it provide performance evaluation metrics?

Yes, it generates specific metrics based on the task, such as accuracy, precision, and recall for classification, or Mean Squared Error and R-squared for regression.

Automated ML Model Trainer

Name: Automated ML Model Trainer
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates the machine learning lifecycle by analyzing datasets, selecting optimal algorithms, and training models with cross-validation.

About

The Automated ML Model Trainer skill empowers Claude to handle end-to-end data science workflows directly within your development environment. By analyzing provided datasets to identify target variables and task types, the skill automatically selects between classification and regression models, configures training parameters, and executes cross-validation. It streamlines the development process by generating detailed performance metrics and persisting trained model artifacts, making it an essential tool for developers building predictive features or benchmarking model performance.

Key Features

Comprehensive performance metrics generation (Accuracy, MSE, R-squared)
Persistence of trained model artifacts for production use
Robust training with integrated cross-validation techniques
Support for classification and regression tasks using scikit-learn
Automated data analysis and target variable identification
0 GitHub stars

Use Cases

Training customer churn prediction models from CSV datasets
Rapidly benchmarking different machine learning algorithms on new data
Forecasting house prices or sales figures using regression analysis

About

Key Features

Comprehensive performance metrics generation (Accuracy, MSE, R-squared)
Persistence of trained model artifacts for production use
Robust training with integrated cross-validation techniques
Support for classification and regression tasks using scikit-learn
Automated data analysis and target variable identification
0 GitHub stars

Use Cases

Training customer churn prediction models from CSV datasets
Rapidly benchmarking different machine learning algorithms on new data
Forecasting house prices or sales figures using regression analysis